New methods combine low and high-fidelity data for accurate surrogate modeling.
problem Challenges in surrogate modeling for high-dimensional outputs with limited training data.
method Projection-based multifidelity linear regression methods integrating low-fidelity and high-fidelity data.
result Multifidelity methods achieve up to 12% improvement in median accuracy compared to single-fidelity methods.
Estimates reliability of nuclear fuel using advanced modeling techniques.
problem Determining the reliability of TRISO-coated particle fuel, which has small failure probabilities and expensive computational models.
method Coupled active learning, multifidelity modeling, and subset simulation.
result Multifidelity modeling strategies consistently reduce the number of high-fidelity model calls.
MF-GLaM models improve stochastic simulator emulation with multifidelity data.
problem Challenging to emulate stochastic simulators' full conditional probability distribution.
method Proposes MF-GLaMs to efficiently emulate HF stochastic simulators using LF data.
result MF-GLaMs achieve improved accuracy or comparable performance at reduced cost.
MFNets constructs efficient multifidelity surrogates from diverse information sources.
problem Creating accurate surrogates from multiple, potentially costly or inaccurate data sources.
method Directed acyclic graph of connections, gradient-based minimization of least squares objective, flexible information source structure.
result Error reduction by orders-of-magnitude, especially in low-data scenarios.
Gradient-enhanced deep GPs improve multifidelity model accuracy.
problem Improving accuracy in multifidelity models using gradient data.
method Extending deep Gaussian processes to incorporate gradient data.
result Gradient-enhanced deep GP outperforms other models in predicting aerodynamic coefficients.
A new method uses multifidelity Gaussian process regression to solve nonlinear PDEs.
problem Efficiently solving nonlinear PDEs using kernel methods.
method Proposes a kernel learning approach based on cokriging for multifidelity simulations.
result Demonstrates improved performance on the Burgers' equation.
New approach uses low-fidelity data to train ML models efficiently.
problem Training ML models with scarce high-fidelity data leads to high variance and poor generalization.
method Multifidelity linear regression using approximate control variates.
result Multifidelity training achieves similar accuracy with reduced high-fidelity data.
Paper introduces a bandit-learning method for multifidelity approximations.
problem Efficiently using data of varying fidelities in scientific computation.
method Formulates multifidelity approximation as a modified stochastic bandit problem and proposes AETC algorithm.
result Established optimality of AETC algorithm for multifidelity approximation.
RAAL optimizes black box function optimization with multifidelity models.
problem Time-consuming and unfeasible black box optimization.
method Resource Aware Multifidelity Active Learning (RAAL) for efficient optimization.
result RAAL optimizes black box function optimization with multifidelity models.
In this work, we propose a framework that combines the approximation-theory-based multifidelity method and Gaussian-process-regression-based multifidelity method to achieve data-model convergence when stochastic simulation models and sparse accurate observation data are available. Specifically, the two types of multifi…
Efficiently estimates rare events using multifidelity modeling.
problem Estimating rare events with computationally expensive models.
method Active learning with multifidelity modeling, adapting the number of high-fidelity simulations based on problem complexity and desired accuracy.
result Significantly reduced the number of high-fidelity model calls while maintaining accuracy.
Two multifidelity trust-region methods use low-fidelity models for efficient optimization.
problem Efficiently solving complex optimization problems with limited data.
method Sketched Trust-Region (STR) and SVD Trust-Region (SVDTR) methods using low-fidelity models.
result Potential gain in efficiency demonstrated through numerical examples.
This paper provides a quantitative method for estimating the risk associated with candidate transportation technology, before it is developed and deployed. The proposed solution extends previous methods that rely exclusively on low-fidelity human-in-the-loop experimental data, or high-fidelity traffic data, by adopting…
New method uses low-fidelity simulations to efficiently infer parameters of high-fidelity models.
problem Challenges in inferring parameters of computationally expensive high-fidelity models.
method Multifidelity simulation-based inference using transfer learning and adaptive selection of high-fidelity parameters.
result Significant reduction in the number of high-fidelity simulations required for inference.
A method to improve surrogate model accuracy using multiple fidelity models.
problem Efficiently combining models of varying accuracy and computational cost.
method Multifidelity Gaussian process models and leave-one-out cross-validation.
result Reduced LOO-CV error at the highest fidelity through adaptive learning.
Efficiently estimates material parameter space with multifidelity Gaussian process modeling.
problem Estimating a region of material parameter space with similar precipitate shapes.
method Multifidelity Gaussian process modeling to reduce computational cost.
result Significant reduction in sampling cost for accurate LER estimation.
A new method for reducing model complexity using neural active manifolds.
problem Uncertainty quantification in computationally expensive models.
method Autoencoders and surrogate models to discover a neural active manifold.
result Neural active manifolds reduce model variance in multifidelity sampling.
This work improves surrogate models using low-fidelity data to enhance accuracy and efficiency.
problem Limited training data makes high-fidelity models unreliable.
method Uses low-fidelity data to augment input space and condition high-fidelity models.
result Increased predictive accuracy and reduced computational cost compared to existing methods.
A cost-effective framework for gradual domain adaptation using multifidelity.
problem Degrading prediction performance due to large domain distance.
method Combines multifidelity and active domain adaptation.
result Improves prediction performance with reduced sample cost.
In this work, we propose a new Gaussian process regression (GPR)-based multifidelity method: physics-informed CoKriging (CoPhIK). In CoKriging-based multifidelity methods, the quantities of interest are modeled as linear combinations of multiple parameterized stationary Gaussian processes (GPs), and the hyperparameters…
BAMS uses Bayesian sampling to discover AV failures more efficiently and accurately.
problem Discovering potential failure cases in autonomous vehicles efficiently and accurately.
method Bayesian adaptive multifidelity sampling (BAMS) prioritizes exploration of low performance regions.
result BAMS discovers 10 times more issues than traditional methods with narrower rate estimates.
Method for creating synthetic multi-fidelity data sets.
problem Lack of representative synthetic datasets for multifidelity optimisation benchmarks.
method Systematic generation of synthetic fidelities from preexisting datasets.
result Allows systematic investigation of lower fidelity proxies' influence.
This paper develops mfEGRA, a multifidelity active learning method using data-driven adaptively refined surrogates for failure boundary location in reliability analysis. This work addresses the issue of prohibitive cost of reliability analysis using Monte Carlo sampling for expensive-to-evaluate high-fidelity models by…
Entropy-based GP adaptive design improves failure probability estimation.
problem Limited accuracy in failure probability estimation due to model evaluation costs.
method Entropy-based Gaussian process (GP) adaptive design combined with multifidelity importance sampling (MFIS).
result More accurate failure probability estimates and higher confidence.
The computational effort for the evaluation of numerical simulations based on e.g. the finite-element method is high. Metamodels can be utilized to create a low-cost alternative. However the number of required samples for the creation of a sufficient metamodel should be kept low, which can be achieved by using adaptive…
In statistical modeling with Gaussian Process regression, it has been shown that combining (few) high-fidelity data with (many) low-fidelity data can enhance prediction accuracy, compared to prediction based on the few high-fidelity data only. Such information fusion techniques for multifidelity data commonly approach …
Automated HPO design using Bayesian optimization and benchmarking.
problem Designing effective hyperparameter optimization algorithms is manual and lacks systematic understanding.
method Formalized space of HPO candidates, Bayesian optimization for search, ablation analysis.
result Simple configurations can perform well in HPO, especially with right parameters.
The key idea of Bayesian optimization is replacing an expensive target function with a cheap surrogate model. By selection of an acquisition function for Bayesian optimization, we trade off between exploration and exploitation. The acquisition function typically depends on the mean and the variance of the surrogate mod…
This work presents a technique for statistically modeling errors introduced by reduced-order models. The method employs Gaussian-process regression to construct a mapping from a small number of computationally inexpensive `error indicators' to a distribution over the true error. The variance of this distribution can be…
Proposes a stratified sampling method for high-dimensional models using neural active manifolds.
problem Uncertainty propagation in computationally expensive models with many inputs.
method Neural active manifolds for nonlinear dimensionality reduction, followed by stratification in the reduced space.
result Effective variance reduction in high-dimensional models using stratified sampling.
Enhances multi-fidelity modeling with DGPs for different input domains.
problem Improving prediction accuracy with multi-fidelity models using different input domains.
method Extends Deep Gaussian Processes (DGPs) to handle different input domains for high and low-fidelity models.
result Demonstrates improved performance on real-world physical problems.
New method improves neural architecture search by optimizing for both performance and diversity.
problem Traditional multi-objective NAS fails to address practical constraints and niches.
method Formulated as quality diversity optimization, introduces multifidelity optimizers.
result Quality diversity NAS outperforms multi-objective NAS in quality and efficiency.
Enhanced Gaussian process regression for multi-fidelity data fusion.
problem Combining data of varying fidelity levels for accurate predictions.
method Gradient-enhanced Cokriging method (GE-Cokriging) for QoI and its gradients.
result GE-Cokriging outperforms conventional multi-fidelity Cokriging in predicting QoI and gradients.
GAR generalizes autoregression for efficient multi-fidelity fusion.
problem Efficiently combining low-fidelity and high-fidelity simulation results.
method Generalized autoregression (GAR) using tensor formulation and latent features.
result GAR outperforms state-of-the-art methods with a large margin in RMSE.
The paper introduces BCART models for aggregate claim amount, improving frequency-severity and joint modeling.
problem Modeling aggregate claim amount with frequency-severity and joint dependencies.
method Developed three types of BCART models: frequency-severity, sequential, and joint models. Used various distributions for claim severity data.
result Weibull distribution outperforms gamma and lognormal for right-skewed, heavy-tailed claim severity data.
The paper uses model-based trees to create interpretable surrogate models for complex machine learning models.
problem Interpreting complex machine learning models.
method Using model-based trees to partition feature space and create interpretable models.
result Model-based trees generate optimal surrogate models that balance interpretability and performance.
Gauge Flow Models use a learnable Gauge Field in Generative Flow Models.
problem Improving generative model performance.
method Integrates a learnable Gauge Field into Flow ODEs.
result Gauge Flow Models outperform traditional Flow Models in Flow Matching experiments.
The study examines how model predictions hold up under model extensions.
problem Model predictions may not be robust under model extensions, limiting their applicability.
method The study uses causal ordering to assess robustness of qualitative model predictions and characterizes model extensions that preserve predictions.
result Conditions and techniques are provided to assess robustness of model predictions under model extensions.
Revises Bayesian model averaging for foundation models.
problem Ensemble pre-trained and lightly-finetuned foundation models for improved classification performance.
method Introduces trainable linear classifiers and computationally cheaper model averaging scheme (OMA).
result Ensembled models can better predict on various datasets.
Paper introduces symmetric divergence link models for probability distributions.
problem Symmetric divergence measures for probability distributions.
method Two general classes of link models: one for survival functions and another for cumulative probability distribution functions.
result Advantages of symmetric divergence measures over asymmetric measures for model averaging and feature assessment.
New method to handle credit portfolio model uncertainties.
problem Model risk in credit portfolio models.
method Demonstrates comprehensive yet easy-to-implement approach to uncertainty in model parameters.
result Comprehensive method to deal with model uncertainties.
The paper tests stock return models and uses LSTM to predict stock returns.
problem Validating stock return models and predicting stock returns.
method Used Fama-French three-factor, four-factor, and five-factor models; also used LSTM model.
result Fama-French five-factor model shows better validity for stock returns.
Researchers review challenges in interpreting additive models, especially neural additive models.
problem Challenges in interpreting additive models, particularly neural additive models.
method Review of generalized additive models and discussion of nonidentifiability.
result Challenges in claiming interpretability or suitability for safety-critical applications of additive models.
Novel hybrid modeling combines ML and physics for real-time diagnosis.
problem Real-time diagnosis of complex systems.
method Combines machine learning and physics-based models to create reduced-order models.
result Generated models are two orders of magnitude simpler, improving efficiency.
CRS model improves ranking data modeling with theoretical guarantees.
problem Lack of rich, multimodal models for ranking data.
method Contextual Repeated Selection (CRS) model for multimodal ranking data.
result CRS model significantly outperforms existing methods in various ranking contexts.
Sigma models linked to Gross-Neveu models via quiver varieties.
problem Understanding the relationship between sigma models and Gross-Neveu models.
method Exploring the mathematical correspondence between sigma models and Gross-Neveu models, including their geometric and trigonometric/elliptic deformations.
result Sigma models are mathematically equivalent to Gross-Neveu models under certain conditions.
Interpretable machine learning has become a strong competitor for traditional black-box models. However, the possible loss of the predictive performance for gaining interpretability is often inevitable, putting practitioners in a dilemma of choosing between high accuracy (black-box models) and interpretability (interpr…
Simple models are preferred over complex models, but over-simplistic models could lead to erroneous interpretations. The classical approach is to start with a simple model, whose shortcomings are assessed in residual-based model diagnostics. Eventually, one increases the complexity of this initial overly simple model a…